Making coffee is a simple task many people do every morning. It’s routine and might be done mindlessly. Imagine if every time you wanted to make coffee, you had to relearn how to use the machine. All of a sudden, a five-minute task might take half an hour every day. Simple skills are now quite complex. You have to determine how many ounces of water to fill the water basin with, replace the coffee filter, carefully calculate the coffee ground to water ratio required to reach your desired brew strength, and depending on the features of the machine, decipher what sequence of buttons to press to initiate the brew. 

That’s exactly what’s happening inside your AI tools every time a routine task gets treated like a brand-new one. When a prompt has to be reevaluated every time for intent, purpose, reference material, and expected output, the AI is essentially relearning how to complete it, resulting in increased compute time and token cost. 

Today, many workers have adapted to using AI and automation in everyday life. From summarizing articles to drafting emails in the early days to now generating complete documents, presentations, and proprietary applications, AI has evolved to complete complex tasks for ordinary people. 

And now that many of us have become reliant on these automated workflows, the cost of using AI has begun to increase. Advanced models and capabilities, such as Microsoft’s Work IQ API and Cowork are now metered on top of the Microsoft 365 Copilot license. As our imaginations and prompts have become more sophisticated, the work your favorite AI model has to do to complete that task has grown significantly and so has the cost of doing it. 

At Collective Insights, we have begun working on ways to make these workflows more efficient and lightweight to maximize AI cost savings. 

You can design and iterate on a skill to map out process, context, and acceptance criteria once, save it as specialized instructions in a skill.md file, and customize code to standardize how parts of the output are calculated and generated. A skill doesn’t waste the expensive model on work a cheaper one can handle, and it checks its own output against acceptance criteria instead of relying on a person to catch mistakes and ask for a redo. The result is fewer retries, fewer relearned prompts, and a smaller footprint per run, every single time the task is fired off.  

Any formatting fixes that used to be applied or reworked by hand, can be addressed at the skill level and handled for good. Every one of those is a lever that pulls compute costs down, and none of them require the underlying AI to get any cheaper. 

As an added bonus, skills are shareable among colleagues, across teams, and deployed to entire organizations, ensuring that employees across practice areas and experience levels produce the same reliable outputs while at the same time minimizing excess AI spend. Further, skills are an open standard used by a range of different AI systems, adopted by Anthropic Claude, GitHub Copilot, and OpenAI Codex CLI, making skills portable between models and ideal for organizations using several platforms. 

The easiest place to start is with the tasks that already have a “right answer”:  

• Document generation that actually follows brand and tone guidelines without a manual cleanup pass 

• PowerPoint decks built from your real templates with layouts that don’t need to be nudged into place 

• Recurring reports where only the numbers should change week to week. 

Once that’s working, the more interesting tasks build upon that logic. 

• A weekly client status report skill validates last week’s actual progress against the project’s milestones and produces the next draft on its own, instead of someone reassembling it from last week’s file.  

• Any operating procedure synthesized from legacy documentation and institutional knowledge when attached to a skill provides an automated check for work done by new hires. 

Skills aren’t the only avenue for getting AI spend under control. Model selection, token allocation, and rethinking a process end-to-end all matter too. But skills are the one your employees can actually see, understand, and adopt without waiting on an IT overhaul, making them one of the most effective places to start. 

None of this requires waiting for AI to get cheaper. It just requires treating the tasks you run every week as processes worth designing once, instead of prompts worth rewriting every time. That’s the difference between paying for a machine that relearns coffee every morning, and one that already knows how you take it.